Committee Specification Draft 01 / Public Review Draft 01 28 October 2016 Specification uris This version

Appendix A.Introduction A.1Terminology

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A.2Normative References
A.3Non-Normative References
Appendix B.Background, Motivation, Principles

Appendix A.Introduction

A.1Terminology

The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be interpreted as described in [RFC2119].

A.2Normative References

[RFC2119] S. Bradner, Key words for use in RFCs to Indicate Requirement Levels, http://www.ietf.org/rfc/rfc2119.txt, IETF RFC 2119, March 1997.

[RDF] RDF 1.1 Primer, W3C Working Group Note 25 February 2014 http://www.w3.org/TR/2014/NOTE-rdf11-primer-20140225/

[RDFS] RDF Schema 1.1, W3C Recommendation 25 February 2014 http://www.w3.org/TR/rdf-schema/

[RelaxNG] http://relaxng.org/

[XSD] XML Schema Part 0: Primer Second Edition, W3C Recommendation 28 October 2004 http://www.w3.org/TR/xmlschema-0/

[XML] Extensible Markup Language (XML) 1.0 (Fifth Edition), W3C Recommendation 26 November 2008 http://www.w3.org/TR/2008/REC-xml-20081126/

[RFC3987] https://tools.ietf.org/html/rfc3987

[CURI] http://www.w3.org/TR/rdfa-syntax/#s_curies

[FRBR] http://www.ifla.org/publications/functional-requirements-for-bibliographic-records

A.3Non-Normative References

[ConsumerRuleML] Consumer RuleML Specification 1.02. http://wiki.ruleml.org/index.php/Specification_of_Consumer_RuleML_1.02

A.4Typographical Conventions

Preformatted type, e.g. Agent, is used for the names of XML components (elements and attributes) and IRIs.

Prefixes used in this document for qualified names in XML and for abbreviating IRIs are the following:

lrml for http://docs.oasis-open.org/legalruleml/ns/v1.0/
lmrlmm for http://docs.oasis-open.org/legalruleml/ns/mm/v1.0/
ruleml for http://ruleml.org/spec
xsi for http://www.w3.org/2001/XMLSchema-instance
xs for http://www.w3.org/2001/XMLSchema
xml for http://www.w3.org/XML/1998/namespace.

The following formatting conventions are used to distinguish the occurrence of LegalRuleML terms within the document:

1. When an occurrence of a term refers to an element or attribute, the term appears in preformatted type, with no spaces and XML markup (e.g. and @hasMemberType).

2. When it refers to a concept, it appears with spaces (if appropriate) and no preformatted type (e.g. Constitutive Statement, Deontic Specification).

3. When both readings are possible, it appears with no spaces, no preformatted type, and no XML markup (e.g. ConstitutiveStatement).

Capitalization is used to distinguish certain terms and names. UpperCamelCase is used for:

Node elements e.g. ;
their associated concepts, e.g. Constitutive Statement, and;
their type, as a metamodel IRI, e.g. lrmlmm:ConstitutiveStatement.

Similarly, lowerCamelCase is used for:

edge elements and attributes e.g., , @memberType;
and their associated role, as a metamodel IRI, e.g. lrmlmm:hasStatement, lrmlmm:memberType.

Italic is used in the angle bracket for distinguishing the annotation to the XML syntax when it is necessary to underline the presence of a generic XML element.

In examples in the presentation syntax, we use a particular annotation:

variables are prefixed with $ (e.g., $income);
constants are prefixed with % (e.g., %employer).

Appendix B.Background, Motivation, Principles

B.1Motivation

Legal texts, e.g. legislation, regulations, contracts, and case law, are the source of norms, guidelines, and rules. As text, it is difficult to exchange specific information content contained in the texts between parties, to search for and extract structured the content from the texts, or to automatically process it further. Legislators, legal practitioners, and business managers are, therefore, impeded from comparing, contrasting, integrating, and reusing the contents of the texts, since any such activities are manual. In the current web-enabled context, where innovative eGovernment and eCommerce applications are increasingly deployed, it has become essential to provide machine-readable forms (generally in XML) of the contents of the text. In providing such forms, the general norms and specific procedural rules in legislative documents, the conditions of services and business rules in contracts, and the information about arguments and interpretation of norms in the judgments for case-law would be amenable to such applications.

The ability to have proper and expressive conceptual, machine-readable models of the various and multifaceted aspects of norms, guidelines, and general legal knowledge is a key factor for the development and deployment of successful applications. The LegalRuleML TC, set up inside of OASIS (www.oasis-open.org), aims to produce a rule interchange language for the legal domain. Using the representation tools, the contents of the legal texts can be structured in a machine-readable format, which then feeds further processes of interchange, comparison, evaluation, and reasoning. The Artificial Intelligence (AI) and Law communities have converged in the last twenty years on modeling legal norms and guidelines using logic and other formal techniques [6]. Existing methods begin with the analysis of a legal text by a Legal Knowledge Engineer, who scopes the analysis, extracts the norms and guidelines, applies models and a theory within a logical framework, and finally represents the norms using a particular formalism. In the last decade, several Legal XML standards have been proposed to represent legal texts [30] with XML-based rules (RuleML, SWRL, RIF, LKIF, etc.) [16, 18]. At the same time, the Semantic Web, in particular Legal Ontology research combined with semantic norm extraction based on Natural Language Processing (NLP) [15], has given a strong impetus to the modeling of legal concepts [8, 10, 11]. Based on this, the work of the LegalRuleML Technical Committee will focus on three specific needs:

To close the gap between legal texts, which are expressed in natural language, and semantic norm modeling. This is necessary in order to provide integrated and self-contained representations of legal resources that can be made available on the Web as XML representations [32] and so foster Semantic Web technologies such as: NLP, Information Retrieval and Extraction (IR/IE), graphical representation, as well as Web ontologies and rules.
To provide an expressive XML standard for modeling normative rules that satisfies legal domain requirements. This will enable use of a legal reasoning layer on top of the ontological layer, aligning with the W3C envisioned Semantic Web stack.
To apply the Linked Open Data [9] approach to model raw data in the law (acts, contracts, court files, judgments, etc.) and to extend it to legal concepts and rules along with their functionality and usage. Without rules that apply to legal concepts, legal concepts constitute just a taxonomy [36].

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